Effective detection of one or more flaws in vehicles, such as rolling stock components in the rail industry, is highly desirable. For example, detection of flaws or problems with the wheels, brake components (including drums, discs, etc.), electronic brake control system, air conditioning units, transmission, driving motors, and/or the like, on rail vehicles is desirable so that corrective action(s) can be taken, e.g., to prevent a derailment, further damage, fire, or the like.
Current detectors include detectors that attempt to detect bearing overheating (e.g., hotbox detectors) and detectors that attempt to detect brake/wheel component overheating (e.g., hot wheel detectors). The rail industry has utilized hotbox detectors for an extended period of time to detect overheating bearings and thereby prevent derailment. These detectors are mounted on the rail or in close proximity to the rail to provide hot bearing and hot wheel data.
However, existing hotbox detectors have a high rate of false positives. Current detectors utilize single-element pyroelectric sensors, quad pyroelectric sensors, or a multi-element linear array of infrared (IR) sensors, each of which is generally an “on” or “off” sensor, to inspect wheels. These sensors do not produce very high signal amplitudes, which makes them relatively insensitive to variations. As a result, very high threshold temperatures are used to limit the number of false positives. The sensors also tend to respond slowly, giving no response of significance if a rail vehicle stops. Furthermore, such sensors do not acquire detailed information on the wheel and surrounding areas (e.g., brake and suspension elements, undercarriage, etc.). As a result of the limited data available from current sensors, sources of noise, outside influences, and other sources of errors, cannot be identified.
The current sensors frequently require that the rail vehicles be moving at a relatively constant speed in order to provide meaningful data. As a result, hotbox detectors are typically installed on a mainline. In response to a hotbox detector indicating the presence of overheating bearings, a train is required to stop so that the hotbox can be inspected. However, any faulty part often cannot be readily repaired. Additionally, a false positive in this scenario can cost thousands of dollars per occurrence due to delays, inspections, disruptions, and the like. For example, an alarm can be triggered by an overheating air conditioning unit on a rail vehicle. In this case, the detector can indicate that a problem exists on a particular rail vehicle. However, the source of the problem can only be determined after an often difficult and time consuming (and therefore costly) hands-on inspection of the rail vehicle. When the source of the alarm does not threaten derailment, as in the case of an overheating air conditioning unit, such an alarm results in significant cost, without a corresponding improvement in safety.
Some approaches seek to utilize signal processing schemes to reduce the number of errors and false positives. For example, one dimensional (1D) signal processing has been proposed to address some errors. However, these approaches fail to provide protection against many false alarms.